Comparative Study of Image Forgery and Copy-Move Techniques

Sridevi, M.; Mala, C.; Sanyam, Siddhant

doi:10.1007/978-3-642-30157-5_71

Cited by 21 publications

(12 citation statements)

References 14 publications

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“…The key feature of digital image cloning is that, because the copied area is selected from the picture itself, the texture, the noise components and color patterns are perfect with the remaining image. Hence, it is not simple to identify the forgery parts [11], [12]. Additionally, there may be post-processing operations which could possibly make the exposing method harder.…”

Section: Figure1-2 Example Of Copy-move Image Forgerymentioning

confidence: 99%

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Kaur¹

2016

IJCA

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With the development of Image processing editing tools and software, an image is manipulated very easily. The image manipulation detection is essential for the reason that an image can be employed as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copymove or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, counterfeiters utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. In the literature part, various researchers portrayed the working scenario of copy-move image forgery utilizing the similarity measures as well as the relationship among the original parts of the image and their pasted parts in the similar image. This research paper illustrates recent issues in the techniques of forgery detection and also all their comparative analysis.

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Section: Figure1-2 Example Of Copy-move Image Forgerymentioning

confidence: 99%

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Kaur¹

2016

IJCA

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“…Sridevi, M.et al [6] surveys different types of image forgeries. This survey has been done on existing forgery detection techniques for images and also highlights some copymove forgery detection methods based on their complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Digital image forgery detection has been growing very fast in the recent years as research domain [5]. Mainly, the digital image forgery is classified in to two categories: copy move forgery or cloning and splicing [6]. In copy move forgery, some content of image is copied and pasted somewhere else in the same image to hide the important information as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Improvement in Copy -Move Forgery Detection Using Hybrid Approach

Saini¹,

Mahajan²

2016

IJMECS

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In this present digital world, digital pictures and videos are the main sources of information. However, these carriers of information can be easily tampered by using softwares such as Adobe photoshop, GIMP etc. Thus, the issue of verification of authenticity and integrity of digital images becomes necessary. Copy Move Forgery is a popular type of forgery that is commonly used for the manipulation of digital images. In this, a region of digital image is copied and then pasted to another location with in the same image with intension to make an object disappear from an image by covering it with small block copied from another part of the same image. There are several post processing operations that are applied by manipulators to obstruct the forgery detection techniques. Thus, for aforementioned problem, we in this paper proposed a method which is a combination of SIFT and SURF algorithms. In this firstly image is split in to sub-parts by DWT method and then SIFT and SURF are applied to actual components of image one by one. After this, features extracted by both methods are matched to locate the forged part in the image. The experiment shows that the proposed method is more efficient and provides better results than applying SIFT and SURF alone.

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“…Active approach embeds data or digital signature into original images via pre-processing, thereby limiting its practical applications. Numerous digital images exist in the Internet without any prior information or digital signature and watermark [4].…”

Section: Introductionmentioning

confidence: 99%

Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

2019

KSII TIIS

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Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis. CMF is performed by copying and pasting a part of an image into other portions of it. Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult. A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks. Secondly, invariant features for each block are extracted using QPCET. Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching. Lastly, a new technique is introduced to reduce the flat region-mediated false matches. Experiments are performed on numerous images selected from the CoMoFoD database. MATLAB 2017b is used to employ the proposed method. Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method. Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression. Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate. The proposed approach may serve as a basis for accurate digital image forensic investigations.

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Comparative Study of Image Forgery and Copy-Move Techniques

Cited by 21 publications

References 14 publications

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

Improvement in Copy -Move Forgery Detection Using Hybrid Approach

Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

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